基本思想:通过darknet在线下进行训练,生成yolov3.weights文件,然后opencv通过线上进行调用,模型可以落地了~~~
#include
#include
#include
#include
#include
#include
#include
using namespace std;
using namespace cv;
using namespace dnn;
vector classes;
vector getOutputsNames(Net&net)
{
static vector names;
if (names.empty())
{
//Get the indices of the output layers, i.e. the layers with unconnected outputs
vector outLayers = net.getUnconnectedOutLayers();
//get the names of all the layers in the network
vector layersNames = net.getLayerNames();
// Get the names of the output layers in names
names.resize(outLayers.size());
for (size_t i = 0; i < outLayers.size(); ++i)
names[i] = layersNames[outLayers[i] - 1];
}
return names;
}
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
{
//Draw a rectangle displaying the bounding box
rectangle(frame, Point(left, top), Point(right, bottom), Scalar(255, 178, 50), 3);
//Get the label for the class name and its confidence
string label = format("%.5f", conf);
if (!classes.empty())
{
CV_Assert(classId < (int)classes.size());
label = classes[classId] + ":" + label;
}
//Display the label at the top of the bounding box
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
rectangle(frame, Point(left, top - round(1.5*labelSize.height)), Point(left + round(1.5*labelSize.width), top + baseLine), Scalar(255, 255, 255), FILLED);
putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 0, 0), 1);
}
void postprocess(Mat& frame, const vector& outs, float confThreshold, float nmsThreshold)
{
vector classIds;
vector confidences;
vector boxes;
for (size_t i = 0; i < outs.size(); ++i)
{
// Scan through all the bounding boxes output from the network and keep only the
// ones with high confidence scores. Assign the box's class label as the class
// with the highest score for the box.
float* data = (float*)outs[i].data;
for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
{
Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
Point classIdPoint;
double confidence;
// Get the value and location of the maximum score
minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
if (confidence > confThreshold)
{
int centerX = (int)(data[0] * frame.cols);
int centerY = (int)(data[1] * frame.rows);
int width = (int)(data[2] * frame.cols);
int height = (int)(data[3] * frame.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;
classIds.push_back(classIdPoint.x);
confidences.push_back((float)confidence);
boxes.push_back(Rect(left, top, width, height));
}
}
}
// Perform non maximum suppression to eliminate redundant overlapping boxes with
// lower confidences
vector indices;
NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect box = boxes[idx];
drawPred(classIds[idx], confidences[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
}
}
int main()
{
string names_file = "/home/oliver/darknet-master/data/coco.names";
String model_def = "/home/oliver/darknet-master/cfg/yolov3.cfg";
String weights = "/home/oliver/darknet-master/yolov3.weights";
int in_w, in_h;
double thresh = 0.5;
double nms_thresh = 0.25;
in_w = in_h = 608;
string img_path = "/home/oliver/darknet/data/dog.jpg";
//read names
ifstream ifs(names_file.c_str());
string line;
while (getline(ifs, line)) classes.push_back(line);
//init model
Net net = readNetFromDarknet(model_def, weights);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(DNN_TARGET_CPU);
//read image and forward
VideoCapture capture(2);// VideoCapture:OENCV中新增的类,捕获视频并显示出来
while (1)
{
Mat frame, blob;
capture >> frame;
blobFromImage(frame, blob, 1 / 255.0, Size(in_w, in_h), Scalar(), true, false);
vector mat_blob;
imagesFromBlob(blob, mat_blob);
//Sets the input to the network
net.setInput(blob);
// Runs the forward pass to get output of the output layers
vector outs;
net.forward(outs, getOutputsNames(net));
postprocess(frame, outs, thresh, nms_thresh);
vector layersTimes;
double freq = getTickFrequency() / 1000;
double t = net.getPerfProfile(layersTimes) / freq;
string label = format("Inference time for a frame : %.2f ms", t);
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255));
imshow("res", frame);
waitKey(10);
}
return 0;
}